\section{Related Work}
\label{sec:related}

\subsection{Mobile Applications}
Many related work has been done to develop various novel
applications on mobile devices. In \cite{ wang:mobileminer}, a real
world data mining tool in mobile communication, MobileMiner, is
presented as a demo to show how data mining techniques can help in
mobile communication data analysis. Yi et al. \cite{ yi:deciphering}
study the characteristics of search queries submitted from mobile
devices using various Yahoo! applications. In \cite{ maekawa:image},
the authors make use of the predefined categories for proper Web
image handing and develop an automatic Web image classification
method to solve the problem of poor input interfaces on mobile
devices.

Mobile users want to access and manipulate information and services
specific to certain situation. In order to manage the mobile
context, a precise definition of shared interfaces is required. In
\cite{mobile_ontology}, the authors provide an overview of the
Mobile Ontology and highlight the advantages gained by defining such
a semantic model. Korpipaa et al. \cite{ Panu:manage} propose a
uniform mobile terminal software framework that provides systematic
methods for acquiring and processing useful context information from
a user's surroundings and giving it to applications.

With the pervasiveness of GPS-enabled devices, several researchers
have done some research about the spatial mining for the mobile
devices. They want to learn knowledge from users' raw GPS data to
provide rich context information for both geographic and mobile
applications. In \cite{ zheng:learning}, the authors propose an
approach based on supervised learning to automatically infer
transportation mode from raw GPS data. In \cite{ zheng:location},
the authors aim to mine interesting locations and classical travel
sequences in a region. Bhuvan et al. \cite{ bamba:supporting}
propose PrivacyGrid, a framework for supporting anonymous
location-based queries in mobile information system to prevent
unauthorized access of his/her location data.

\subsection{Graph query}
The issue of graph query is crucial in many applications, such as
bioinformatics, web exploration, social network analysis, and etc.
Agrawal et al. \cite{ agrawal:fast} propose Algorithm Apriori to
solve the problem of discovering association rules between items in
a large database. In \cite{ inokuchi:an} and \cite{
vanetik:computing}, the authors design an apriori-based algorithm to
efficiently mine the association rules among the frequently
appearing substructures in a given graph data set.

In semistructured/XML databases, query languages built on path
expressions become popular. Kuramochi et al. \cite{
kuramochi:frequent} show that in a given graph model, the problem of
frequent pattern mining becomes the problem of discovering the
subgraphs that occur frequently over the entire set of graphs. In
the paper, the authors present an algorithm, called FSG to find
frequent subgraphs in large databases. D(k)-index \cite{
qun:d(k)-index:} is an adaptive structural summary for
graph-structured data. Based on the concept of bisimilarity,
D(k)-index possesses the adaptive ability to adjust its structure
according to the current query load.

In \cite{ washio:state}, the paper has a general introduction on the
recent progress of graph-based data mining. Yan and Han et al.
\cite{ yan:gspan:}\cite{ yan:closegraph:} apply the pattern-growth
approach to directly generate frequent subgraphs. In \cite{
yan:graph}, Yan and Han propose a novel graph indexing model based
on discriminative frequent structures that are identified through
the pattern-growth mining approach. In \cite{ kuramochi:finding},
the paper presents two algorithms based on horizontal and vertical
pattern discovery paradigms to find the connected subgraphs in a
single large undirected labeled sparse graph. Hasan et al.
\cite{zaki:dmtl} and \cite{zaki:towards} develop a generic pattern
mining library which provides the framework for mining a large
spectrum of patterns including itemset, sequence, tree and graph.
